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Designing Affirmative Action Policies under Uncertainty

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Title: Designing Affirmative Action Policies under Uncertainty
Language: English
Authors: Hertweck, Corinna (ORCID 0000-0002-7639-2771), Castillo, Carlos (ORCID 0000-0003-4544-0416), Mathioudakis, Michael (ORCID 0000-0003-0074-3966)
Source: Journal of Learning Analytics. 2022 9(2):121-137.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 17
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Affirmative Action, Policy Formation, Educational Policy, College Admission, Policy Analysis, Prediction, Artificial Intelligence, Social Justice, Data Use, Foreign Countries, Social Differences
Geographic Terms: Chile
ISSN: 1929-7750
Abstract: We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1358965
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Designing Affirmative Action Policies under Uncertainty
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  Data: <searchLink fieldCode="AR" term="%22Hertweck%2C+Corinna%22">Hertweck, Corinna</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7639-2771">0000-0002-7639-2771</externalLink>)<br /><searchLink fieldCode="AR" term="%22Castillo%2C+Carlos%22">Castillo, Carlos</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4544-0416">0000-0003-4544-0416</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mathioudakis%2C+Michael%22">Mathioudakis, Michael</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0074-3966">0000-0003-0074-3966</externalLink>)
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: 17
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  Data: <searchLink fieldCode="DE" term="%22Affirmative+Action%22">Affirmative Action</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+Formation%22">Policy Formation</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Policy%22">Educational Policy</searchLink><br /><searchLink fieldCode="DE" term="%22College+Admission%22">College Admission</searchLink><br /><searchLink fieldCode="DE" term="%22Policy+Analysis%22">Policy Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Justice%22">Social Justice</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Differences%22">Social Differences</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Chile%22">Chile</searchLink>
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  Data: 1929-7750
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  Data: We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.
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  Data: 2022
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  Data: EJ1358965
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 121
    Subjects:
      – SubjectFull: Affirmative Action
        Type: general
      – SubjectFull: Policy Formation
        Type: general
      – SubjectFull: Educational Policy
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      – SubjectFull: College Admission
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      – SubjectFull: Policy Analysis
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      – SubjectFull: Prediction
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Social Justice
        Type: general
      – SubjectFull: Data Use
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Social Differences
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      – SubjectFull: Chile
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      – TitleFull: Designing Affirmative Action Policies under Uncertainty
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              Y: 2022
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